Artificial Intelligence in Supply Chain Management

The keys to the implementation of artificial intelligence (AI) in end-to-end supply chain management.

In recent years, artificial intelligence (AI) has gained impact and visibility in both business and academic or informational fields. But what exactly do we understand by AI? We found several definitions. On the one hand, Robert defines AI as “The area of ​​study that seeks to explain and emulate intelligent behavior in terms of computational processes.” This definition refers to a system that acts rationally. On the other hand, Karol states that AI is “the simulation of human intelligence, incorporating reasoning, perception, problem-solving and planning”.

In a broad sense, the concept of AI refers to intelligent systems that can think and learn, which helps the human being in decision making and execution of actions. AI comprises a number of techniques. UPS also implemented Artificial intelligence in its supply chain.

Although AI has been developing for several years, its implementation in the day-to-day operations of companies is one of the most revolutionary changes that organizations are experiencing in decades. This is mainly due to three factors. On the one hand, the enormous storage capacity of our systems; on the other, the large amount of data available; and, finally, the availability of great processing capacity of that data. These three elements allow the application of algorithms in a very powerful and efficient way. These data are processed by the algorithms and produce a result that is evaluated, and the system learns from that result, improving future outputs according to established parameters.

The implementation of AI impacts the entire value chain of companies, and especially the management of supply chains, from end to end, helping to substantially improve the decision-making process.

All this allows improving not only the physical flow of materials and products but also the flow of information and the financial flow, producing a substantial improvement in productivity in all elements of the chain through greater visibility among all agents, improving processes and product quality and achieving an increase in customer service, reducing errors and
defects.

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Supply chains generate data every time activity or process is performed, or a product or service is obtained. This data can be stored and used to feedback the AI ​​systems that will generate better results in the future.

Repetitive activities, movements of products and materials, transport and highly predictive and very frequent activities are the first candidates to benefit from the automation offered by AI, which will generate the replacement of low-skilled labor by another type of labor that Has the ability to interact with these systems. But more important than this change in the profile of the workforce is the ability of AI to raise and improve the quantity and quality of work that people do in organizations today, producing a significant increase in productivity. Within this category, we can find many activities in the areas of Production, Transportation and Storage, as well as back-office tasks, such as billing portal, finance, legal, human resources, etc.

The increase in productivity due to the implementation of AI can favor the increase in cases of back shoring or registration reshoring in developed countries. That is, companies that have relocated in recent decades, seeking cost savings, will have reason to rethink their facilities location strategy and evaluate the possibility of installing their production centers again in the countries of origin since productivity that AI will bring will be much greater than productivity in low-cost labor countries. This relocation register will default cause a need for labor, in the manufacturing sector in developed countries, for profiles with specific training in the management of these technologies.

AI also allows us to perform the simulation of scenarios to compare costs, times and productivity and assess risks, customer service, benefits and also CO2e emissions. The generation of these scenarios will allow AI systems to make recommendations so that supply chain managers can make decisions based on accurate Payroll Login and reliable information.

The ability to link and integrate the different areas and activities of the supply chain into a system that allows predicting failures and defects using AI has great value for the entire chain management process, reducing downtimes in production systems and Logistics To be able to predict the demand with systems that learn from the variations produced in the markets, in the points of consumption or in the devices through the Internet of Things (IoT) will allow creating adaptable and flexible systems. These will be able to accommodate their production and transportation capacity to the real needs of their customers, eliminating not only the problematic whip effect but also optimizing machine and operator times and reducing unnecessary inventories, avoiding stock breakage. The prediction will also be related to the risks of disruption that may occur in the management of the supply chain.

AI has to be an agent that allows developing supply chains that work transparently, without interventions. Although the idea of ​​a completely autonomous supply chain is still far away, AI can allow us to increase, complement, assist, predict, improve and measure the performance of current workers and teams in each of the functions performed in the chain, optimizing times and resources to obtain greater productivity. In addition, AI can help with other functionalities for better decision making in the daily operation of the supply chain, identifying patterns and preferences that allow companies to deliver products more suited to the needs of customers in less time, adding value and improving customer service.

Therefore, organizations have to prepare to adopt this change and lead it within them, collaborating with their suppliers and customers. Supply chain leaders have to take advantage of all available data, information and technologies to increase productivity and create value for all stakeholders.

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This change that AI will produce in supply chains has to be led by companies, which must develop a model of integration of people with the technology, equipment, and machines that will interact. The organization must be prepared to manage this change. The implementation of AI is not a project with a clearly detailed and temporal scope and with defined resources. It is rather a path that companies must begin to walk, experiment, adapt, correct and iterate as the environment requires.

How to Implement Ai in Supply Chains?

Surveys of executives from various sectors show that a large majority ensures that AI will eventually be implemented in their companies. But they recognize that, at present, there are a number of barriers that slow, hinder or even fail to some extent, these initiatives. Among these can be found the difficulty to integrate AI projects with the processes or systems currently implemented, the lack of knowledge of these technologies by the management of the company, the high costs, the low qualification of the personnel and the incipient state of these technologies.

To implement AI in our supply chain it is necessary to make an analysis of the state of the chain from end to end, considering all the agents involved. In this way, we can define the needs and opportunities that can be created in the implementation process. This analysis process should use a gradual approach, which will improve current capacities instead of replacing them, and incorporate the following pillars:

• Establish an AI strategy – This strategy has to be aligned with the company’s global strategy. AI is a very powerful toolset. It may be the most important that a company has been able to have in its entire history, but that is still a set of tools. The first step should be to understand these technologies well. Supply chain managers have to reflect on what they want AI for. How does AI adapt to current processes? Is it necessary to redesign the processes? As at the time of installing an ERP, it is first necessary to ask ourselves why we do what we do in the supply chain. In this reflection, suppliers and customers have to participate from end to end and try to identify the alignment between all agents on a common AI strategy.
• Decide what type of AI the company needs to meet its strategy – Any technology that we apply has to be oriented to the contribution of value, which may come from an improvement in the customer experience or by increasing productivity and reducing waste in the processes. It is important to establish what the needs are and define what are the areas or activities in the chain that are potentially “ quick wins ”, that is, what are the technologies within the AI ​​that allow me to obtain the maximum benefit with the least investment in costs, time, capabilities and resources.
• Study how to integrate technology into the current environment of the company – Once the technology we want to work with is chosen, it is convenient to design a pilot implementation project. It is important to understand the level of maturation and technological integration among all agents in the supply chain and also their level of automation. In many cases, there may be different levels of automation that do not allow the flow of information and materials in a homogeneous way. There are also sectors that are more open to technological changes, and other sectors that, by their nature, are not. This is closely related to risk perception and tolerance. One of the ways to introduce companies in the way of AI is through the development of the capabilities to manage these technologies. Companies can do this through internal development, building capacities through training or through the hiring of experts in this area that can mark the path where the particular AI strategy should be directed. Another alternative would be through collaborations with technological partners that allow acquiring this knowledge.
• Measure the improvements – The implementation of any system must have metrics that allow performance evaluation, and AI is no exception. It is important to establish indicators that are robust for a correct evaluation of the performance of the tools. It may be the case that companies already have indicators that measure the performance of the chain. In this situation, it is only necessary to establish the improvement objectives on these indicators, which will be given by the implementation of AI in the information flow and the physical flow. Therefore, we will have to establish improvement objectives in the efficiency and productivity metrics of supply chains, such as inventory turnover, inventory days, service level, fulfillment, levels of waste and obsolescence, among others.
• Adjust and scale – Continuous improvement is a key aspect of AI systems: being able to learn from the environment and prepare a new solution to the same problem is one of the advantages that this technology allows us. This will make the components of the supply chain that use AI more intelligent. In the learning process, it is important to identify biases that cause the system learning to be inadequate. It is also essential to be able to scale the systems to other areas and other agents that allow creating value incrementally throughout the supply chain.

Application Cases

There are many and varied applications of AI in supply chains. We list some of them below:

1. Prediction – Predict the size or location of the next order to adapt the equipment, machinery, personnel and resources to that demand in the place and the quantities that are needed. Companies will be more agile and can respond better to customer needs. Traditionally, models based on historical data or experience of professionals in the sector have been used. Currently, there are tools based on machine learning that analyze dozens of parameters to be able to predict, for example, initially unexpected spikes in demand, or delays in delivery times, and plan supplies and deliveries with much more reliability. This is done at very high speeds, determining for each type of product (or family of products) which algorithm minimizes errors and optimizes the result. Continuously, the accuracy of the forecasts is monitored, so that the systems learn from themselves and make decisions to make the necessary adjustments. Not only are they able to predict, but they identify the factors that influence, propose solutions and learn from these situations.
2. Operations planning – The AI ​​allows for better production planning, managing all restrictions that allow reducing waste in operations and improving the flow of materials and products, with the consequent reduction of inventory in the process. It also helps to anticipate and properly plan the customization of the product, enhancing postponement processes and maintaining efficiency with high levels of customer satisfaction.
3. Logistic routes – Tools for the intelligent optimization of the logistics routes of freight transport. In the design of the routes, an infinite number of factors must be taken into account, such as loads, volumes and weights, pick-up and delivery schedules, transport times and distances, customer availability windows, immediate deliveries, specific needs of customization, weather conditions, traffic, possible changes for regulatory reasons, etc. The optimization of routes in the transport network has an impact on a lower cost and also on the reduction of CO2e emissions. This optimization will allow finding more productive solutions in long-distance transport, but also in the distribution of the last mile, to offer better shipping times at a lower cost per unit.
4. Risk management – Tools for assessing risk management in the supply chain. Throughout the chain, there are countless risks that can impact the continuity of operations. AI-based tools are already applied in various sectors, such as automotive, aeronautics and technology, to try to assess the degree of resilience of organizations and networks of hundreds of suppliers located throughout the world. These tools based on machine learning and natural language processing technologies propose agile solutions that reduce or eliminate risks.
5. Automation and robotics – All functions that are repetitive within operations and supply chains are likely to be automated, generating an increase in productivity. The application of robots is already a very common element in many manufacturing and storage facilities. These devices may also be endowed with “intelligence” in decision making to optimize movements. For example, an American chain of department stores uses robots to help customers in case of doubt, as well as to locate items that they cannot find for themselves. These robots use deep learning algorithms, computer vision and natural language processing systems to interact and understand customers, as well as to recognize the areas through which they move and identify items in the warehouse and shelves. Another example of an application would be the use of automated systems combined with high-speed sorting robots on conveyor belts. Users of this technology are the logistics operators that manage high volumes of envelopes, packages and even pallets, or waste and recycling management companies in which various waste is separated based on their nature, all done at high speeds and with A high level of precision.
6. Autonomous vehicles – Autonomous vehicles will be an important advance in supply chains. Although the environment is not yet ready to apply this technology to its full potential, we will see important applications, such as, for example, autopilot systems and driving assistance that will reduce the consumption of fuel, improve times, reduce CO2e emissions and provide greater security to the entire environment. Pilot tests are currently being carried out in the transport of small-volume goods (such as parcels or fast-food deliveries at home), as well as in the transport of large-tonnage truck loads and long distances. Autonomous vehicle technology is also applied to equipment that handles goods in truckloads and unloadings, as well as in movements within the warehouse.
7. Computer vision and voice recognition – These systems allow the improvement of operations within warehouses and distribution centers, helping operators to the tasks of order preparation ( picking ) and reducing the level of error to a minimum. They are also used to optimize the use of space or volume both in a warehouse and on the shelves of a store in retail or supermarket sectors.
8. Visual inspection – Both in logistics environments and in Production areas, AI systems allow us to detect and identify possible damages, errors, defects and wear of products. But they not only carry out the identification but also classify the type of damage and make the most appropriate corrective decisions to correct the defect or error (labeling, removing it from the production line, communicating with other areas of the company to notify the fault, make adjustments in equipment, etc.).
9. Maintenance of the installations – The AI ​​allows analyzing when will be the best time to perform the preventive maintenance of machinery and equipment to reduce the time they are not producing. Similarly,  workers in a productive environment, equipped with mobile devices with camera and deep learning technology, they can photograph the components that give them failure or error, and an AI system will give them the appropriate explanations to correct the error. The interaction with this platform can be by voice, which allows them freedom in their hands to manipulate and execute the operations necessary to solve the problem. This application aimed at supporting maintenance activities can also provide this service in productive operations. Learning the  AI systems applied in corrective and preventive maintenance will allow them to learn and develop predictive maintenance programs.
10. AI application as ‘back-office’ – Throughout the supply chain there are a series of high-volume administrative tasks, which are repetitive and require some precision and detail. Therefore, they can be the source of errors with a non-negligible impact on legal, accounting and financial issues, which can result in bad customer experience. Beginning to use AI systems that combine robotic process automation (RPA), cognitive automation or natural language processing, with the aim of improving process efficiency, minimizing the number of errors and reducing costs. The goal today is not so much the immediate replacement of people by robots, but to let them do the repetitive tasks and that human beings take care of the most complex tasks, the interpretation of results and decision-making.
11. Improvement of the customer experience. AI can be a valuable tool for customer service management and personalized marketing. Voice recognition teams today help, in a significant way, to improve the customer experience of companies. These systems provide customers with information on the status of their shipments, as well as establish conversations with them to manage unforeseen events, changes in late deliveries or incident management and feedback.by these. More advanced systems manage countless variables that allow them to “predict” with a very small margin of error (this is measured by the number of claims or returns) customer needs. Thus, they carry out what is called an anticipation logistics, which begins to manage the shipment of the goods when the order for it has not even started.

Final considerations

Today, a large number of practical applications of AI systems can be found. Although some of them are presented in an incipient state, there is no doubt that, in the coming years, this growth will be consolidated hit. In this article, some barriers to its implementation have been identified, as well as some factors or pillars to take into account so that it is carried out successfully.

We can clearly see that the application of AI in supply chain management is a generator of value creation for companies through improved productivity, better customer service, better working conditions for employees in all chain levels and emission reduction.

AI has to be aligned with all agents in the supply chain in order to get the most out of all these technologies. AI can be a very useful tool to improve the operational decision-making process in production and logistics processes.

Many governments in developed countries are implementing an AI strategy to help their companies compete in highly technological environments. It is important that these strategies accompany the needs of the companies and become a support for the application of AI, especially in small and medium enterprises, which generally represent a large part of the gross product of these countries.

Finally, the implementation of AI in supply chains has to take into account the safety of the people and systems with which it operates from end to end. This requires computer security systems that ensure the integrity and maintenance of the data generated and stored.

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